Papers

Learn more about AI2's Lasting Impact Award
Viewing 1-10 of 805 papers
  • Continued Pretraining for Better Zero- and Few-Shot Promptability

    Zhaofeng Wu, Robert L. Logan IV, Pete Walsh, Akshita Bhagia, Dirk Groeneveld, Sameer Singh, Iz BeltagyEMNLP2022 Recently introduced language model prompting methods can achieve high accuracy in zero-and few-shot settings while requiring few to no learned task-specific parameters. Never-theless, these methods still often trail behind full model finetuning. In this work…
  • Exploring The Landscape of Distributional Robustness for Question Answering Models

    Anas Awadalla, Mitchell Wortsman, Gabriel Ilharco, Sewon Min, Ian H. Magnusson, Hannaneh Hajishirzi, Ludwig SchmidtFindings of EMNLP2022 We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question answering datasets, including a di-verse set of architectures, model sizes, and…
  • Hyperdecoders: Instance-specific decoders for multi-task NLP

    Hamish Ivison, Matthew E. PetersFindings of EMNLP2022 We investigate input-conditioned hypernetworks for multi-tasking in NLP, generating parameter-efficient adaptations for a decoder using a hypernetwork conditioned on the output of an encoder. This approach produces a unique decoder for every input instance…
  • Lila: A Unified Benchmark for Mathematical Reasoning

    Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, Ashwin KalyanEMNLP2022 Mathematical reasoning skills are essential for general-purpose intelligent systems to perform tasks from grocery shopping to climate modeling. Towards evaluating and improving AI systems in this domain, we propose LILA, a unified mathematical reasoning…
  • Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes

    Kaige Xie, Sarah Wiegreffe, Mark O. RiedlFindings of EMNLP2022 Multi-hop Question Answering (QA) is a chal-lenging task since it requires an accurate ag-gregation of information from multiple context paragraphs and a thorough understanding of the underlying reasoning chains. Recent work in multi-hop QA has shown that…
  • Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems

    Yoshitomo Matsubara, Luca Soldaini, Eric Lind, Alessandro MoschittiFindings of EMNLP2022 Large transformer models can highly improve Answer Sentence Selection (AS2) tasks, but their high computational costs prevent their use in many real-world applications. In this pa-per, we explore the following research question: How can we make the AS2 models…
  • Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning

    Oyvind Tafjord, Bhavana Dalvi Mishra, Peter ClarkEMNLP2022 Our goal is a question-answering (QA) system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning . Such a capability would allow better understanding of why a model produced the answer it did. Our approach…
  • GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation

    Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel S. WeldEMNLP2022 While often assumed a gold standard, effective human evaluation of text generation remains an important, open area for research. We revisit this problem with a focus on pro-ducing consistent evaluations that are reproducible —over time and across different…
  • How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers

    Michael Hassid, Hao Peng, Daniel Rotem, Jungo Kasai, Ivan Montero, Noah Smith, Roy SchwartzEMNLP Findings2022 The attention mechanism is considered the backbone of the widely-used Transformer architecture. It contextualizes the input by computing input-specific attention matrices. We find that this mechanism, while powerful and elegant, is not as important as…
  • In-Context Learning for Few-Shot Dialogue State Tracking

    Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, Tao Yu, Noah A. Smith, Mari OstendorfEMNLP Findings2022 Collecting and annotating task-oriented dialogues is time-consuming and costly. Thus, zero and few shot learning for dialogue tasks presents an exciting opportunity. In this work, we propose an in-context (IC) learning framework for zero-shot and few-shot…